发表机构
University of Macau（澳门大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Nomad框架，通过相对转移替代绝对目的地，分离移动行为学习与位置落地，实现无目标轨迹的城市间移动生成，在十城市迁移实验中优于基线。
AI 中文摘要
个体移动轨迹支撑着城市分析和基于位置的服务，然而大多数轨迹生成器需要其部署城市的观测数据。这一假设恰恰排除了那些无法获得轨迹的城市，尽管兴趣点（POI）及其属性可以从公共地图中获取。我们研究无目标轨迹生成：从源城市的POI和轨迹中学习，同时仅利用目标城市的POI坐标和类别，且没有目标轨迹或轨迹衍生统计量可用于训练、模型选择或生成。现有轨迹生成器通常预测绝对目的地，将可复用的移动行为与城市特定的POI身份和空间布局纠缠在一起。我们的核心见解是用上下文条件的相对转移来取代这种受城市束缚的输出。我们提出Nomad，一个迁移与落地框架，将学习人们如何移动与确定这些移动在何处实现分离开来。具体而言，一个历史条件流匹配模型从源轨迹中学习一个关于POI上下文之间语义位移、地理位移和经过时间的转移先验；在推理时，一个行为图和一个探索-返回游走将采样的转移落地到目标POI地图上。这种分解使得能够直接测试表示层面的可迁移性，而无需假设整个移动分布的不变性。在十个城市和14次迁移上的广泛实验表明，Nomad在轨迹保真度和下游效用方面优于适应基线，在分布保真度上平均误差比每个指标的最佳基线降低约15%，在下游效用上降低约3%。
英文摘要
Individual mobility trajectories support urban analysis and location-based services, yet most trajectory generators require observations from their deployment city. This assumption excludes precisely the cities where trajectories are unavailable even though points of interest (POIs) and their attributes can be obtained from public maps. We study target-trajectory-free generation: learning from POIs and trajectories in source cities while utilizing only POI coordinates and categories in a target city, with no target trajectory or trajectory-derived statistic available for training, model selection, or generation. Existing trajectory generators typically predict absolute destinations, entangling reusable movement behavior with city-specific POI identities and spatial layouts. Our core insight is to replace this city-bound output with context-conditioned relative transitions. We propose Nomad, a transfer-and-ground framework that separates learning how people move from determining where those movements are realized. Specifically, a history-conditioned flow-matching model learns from source trajectories a transition prior over semantic displacement between POI contexts, geographic displacement, and elapsed time; at inference, a behavior graph and an exploration--return walk ground sampled transitions onto the target POI map. This factorization enables a direct test of representation level transferability without assuming invariance of the full mobility distribution. Extensive experiments across ten cities and 14 transfers show that Nomad outperforms adaptation baselines in trajectory fidelity and downstream utility, lowering the average error over the best baseline of each metric by about 15% in distributional fidelity and about 3% in downstream utility.
